As a general class, constrained local models (CLMs) are conventional methods of finding sets of points on a subject image, as constrained by a statistical shape model. Generally, a region from an image is sampled and projected onto a reference frame to generate a response image using local models, where each point has a cost for its estimated location. In the case of an image of a human face and the pose of the associated human head, the parameters of the statistical shape model, and sometimes pose models, are then varied to find an optimal combination of points that minimizes the total cost, thereby representing the face and the head pose in the image.
One main drawback of conventional CLM schemes for modeling facial features is a limited range of working head rotations in the models. The 2-dimensional (2D) context of each landmark point changes drastically with changes in the “yaw” and “pitch” angles of the 3D head pose, for example. Moreover, landmark points become occluded and invisible in the image for highly rotated head positions. Unfortunately, increasing the number of classes to represent more head rotation states requires an enormous amount of work to construct the needed database for training the local models. To manually mark enough training images to populate a comprehensive set of head rotation classes is far from cost effective or desirable, and is conventionally avoided. Conventional face tracking schemes are also slower and more complex during runtime when they are made to rely on larger working databases, more machine learning, or more training of conventional modeling schemes.